ML Architect: Production AI Systems & Pipelines

DISCOVERED; Abu Dhabi; On-site; AED 250,000 - 420,000; Full time

Job summary

DISCOVERED is establishing an AI Centre of Excellence in Abu Dhabi, driving AI from pilot projects to scaled, production-ready solutions across core functions. The role focuses on building scalable ML systems and pipelines to enable real-world business impact.

The Machine Learning Engineer will own the full ML lifecycle—from data handling and model training to deployment and optimisation in production, including fine-tuning LLMs/SLMs, model serving APIs, and integrating models with broader

Qualifications

• 3-7 years' experience building scalable, production-grade AI or ML systems.
• Strong experience across supervised and unsupervised learning, deep learning, NLP, CV or generative AI.
• Strong ML systems architecture knowledge.
• Hands-on experience fine-tuning and deploying LLMs or SLMs.
• Experience with model serving and API development using FastAPI or Flask.
• Good understanding of Docker, Kubernetes, CI/CD and MLOps tooling such as MLflow or Kubeflow.
• Experience deploying ML workloads on AWS or Azure.
• Bachelor's degree in Computer Science, Engineering or a related field.

Responsibilities

• Design, build and deploy scalable ML systems for business problems.
• Cover the full ML lifecycle from data handling to deployment and optimisation.
• Collaborate with DS, Engineering and Product teams to integrate models into applications.
• Fine-tune and deploy LLMs/SLMs and build model-serving APIs; improve real-time and batch inference.

Skills

ML systems architecture

Model deployment

API development

MLOps tooling

Cloud deployment (AWS/Azure)

Education

Bachelor's degree in Computer Science or Engineering

Master's degree or PhD in CS/AI (plus)

Tools

Docker

Kubernetes

CI/CD

MLflow

Kubeflow

FastAPI

Flask

AWS

Azure

Job description

DISCOVERED is establishing an AI Centre of Excellence in Abu Dhabi, driving AI from pilot projects to scaled, production-ready solutions across core functions. The role focuses on building scalable ML systems and pipelines to enable real-world business impact.

The Machine Learning Engineer will own the full ML lifecycle—from data handling and model training to deployment and optimisation in production, including fine-tuning LLMs/SLMs, model serving APIs, and integrating models with broader